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AIP Evolve — our new product for making agents more efficient and cost effective. See how Chad and Colton used it to autonomously swap models, tune prompts, validate outputs, and find structured ontology data that eliminated 2 LLM calls; cutting compute costs while improving accuracy and reliability in production.

Palantir

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159,367 просмотров • 2 месяцев назад •via X (Twitter)

Наука и технологии Финансы

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See how Palantir Software Engineer Colton Rusch uses AIP Evolve to prove that cheaper AI doesn't have to mean worse AI: "Evolve screened a whole suite of models across providers, then used evals and expert feedback to rule out certain options and keep others for further optimization." "We got to the point where evolve produced a vastly cheaper version — 68% lower compute costs." "We got lower costs and better quality at the same time."

See how Palantir Software Engineer Colton Rusch uses AIP Evolve to prove that cheaper AI doesn't have to mean worse AI: "Evolve screened a whole suite of models across providers, then used evals and expert feedback to rule out certain options and keep others for further optimization." "We got to the point where evolve produced a vastly cheaper version — 68% lower compute costs." "We got lower costs and better quality at the same time."

Palantir

13,673 просмотров • 27 дней назад

Tokenmaxxing is for stupid people. Chad Wahlquist says Palantir’s AIP Evolve helps guide you to pick which model is actually the most efficient and cost-effective: “McCarthy talked about this—in 2 days they were able to eliminate 60% of their token cost.” Via TBPN

Tokenmaxxing is for stupid people. Chad Wahlquist says Palantir’s AIP Evolve helps guide you to pick which model is actually the most efficient and cost-effective: “McCarthy talked about this—in 2 days they were able to eliminate 60% of their token cost.” Via TBPN

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19,736 просмотров • 2 месяцев назад

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

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Avalanche🔺

22,981 просмотров • 1 год назад

We’re expanding our strategic partnership with Microsoft to: ⚡ Deliver OpenAI’s state-of-the-art models in Snowflake Cortex AI on Microsoft Microsoft Azure. Customers will soon be able to build AI-powered data agents to run analytical workflows on structured and unstructured data using OpenAI’s models. ⚡ Make Snowflake Cortex Agents available in Microsoft 365 Copilot and Microsoft 365 apps, ensuring AI insights become more accessible for users and enabling better decision-making across the enterprise. Learn more about how we’re bringing easy, efficient, and trusted AI to enterprises around the world:

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Snowflake

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The Chairman and CEO of AIG spent over a minute talking about how critical the Ontology is to deploying LLMs in the enterprise 💪 “Ontology is critical for deploying large language models. It brings together the relevant data sets that define the components of our insurance business, integrates and sequences them and then models how they relate to one another. Our ontology will create a clear record of any actions taken, which will inform business logic and provide the ability to audit agents activities.”

The Chairman and CEO of AIG spent over a minute talking about how critical the Ontology is to deploying LLMs in the enterprise 💪 “Ontology is critical for deploying large language models. It brings together the relevant data sets that define the components of our insurance business, integrates and sequences them and then models how they relate to one another. Our ontology will create a clear record of any actions taken, which will inform business logic and provide the ability to audit agents activities.”

Chad Wahlquist

23,869 просмотров • 1 год назад

Structured Outputs: The Building Blocks for Reliable AI! 🏗️ I am SUPER EXCITED to publish our newest Weaviate Podcast featuring Will Kurt (Will Kurt) and Cameron Pfiffer (Cameron) from .txt! 🎙️🎉 Dottxt is the company behind Outlines, reshaping how we control LLM outputs with constrained decoding! 🚀 This podcast dives into all sorts of details from new applications in metadata and information extraction, such as self-expanding knowledge graphs, to structured reasoning, report generation, and more! We also dove into the details of how this leverages Automata Theory and Finite State Machines to achieve this, how it is packaged with inference engines such as vLLM, and a rebuttal to Let Me Speak Freely, discussing how Structured Generation actually improve the quality of LLM outputs, in addition to speeding up inference, and of course, making it more reliable! I hope you find the podcast interesting, this was a super fun one! Links below!

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Palantir

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